Feature Request: Support ignore_index / ignore_class in Losses and Metrics

[jermmy19998]

In practical medical image segmentation, it is very common to have label values that should be ignored (e.g. padding, unlabeled regions, auxiliary classes).

Currently, MONAI losses and metrics (e.g. DiceLoss, DiceMetric, MeanIoU) do not natively support ignoring specific classes or label values. Users must manually mask predictions and targets, which is error-prone and inconsistent.

Request:
Add native support for an argument such as ignore_index or ignore_classes to losses and metrics.

Example:

DiceCELoss(include_background=False, ignore_index=2)
DiceMetric(include_background=False, ignore_index=2)

Expected behavior:

Voxels with ignored labels are excluded from numerator and denominator

Works consistently for binary and multi-class segmentation

Compatible with reduction="mean" and get_not_nans

This would improve correctness, reproducibility, and align MONAI with common PyTorch APIs (e.g. CrossEntropyLoss(ignore_index)).

Thanks for considering!